With continuous development and improvement of computer technologies, in a process of using a terminal application, a background server not only ensures normal running of the terminal application, but also recommends related service data. For example, a music application recommends a daily selected song, or a shopping application recommends a hot product, thereby improving user experience.
An existing recommendation architecture is formed by two parts: an offline layer and a real-time layer. The offline layer is responsible for collecting behavior operations of users for current services in a period of time, to calculate a periodic service attribute such as a label attribute of each user or similar data, and also needs to be responsible for selecting recommendation service data. The real-time layer is responsible for further filtering the recommendation service data, and outputting the filtered service data to a user terminal, and also needs to perform feedback learning according to real-time behavior operations of the users for the current services, to generate a real-time service attribute such as a real-time label attribute of each user. Because the offline layer has a large calculation burden, the update efficiency of the recommendation service data is easily affected, and because a cycle for the offline layer to generate the recommendation service data is long, and a service attribute obtained by the real-time layer through real-time feedback learning usually cannot take effect until the offline layer selects the recommendation service data next time, the recommendation service data cannot be adjusted in real time, thereby affecting the effect of service data recommendation.